Session ID: 
7

Validation and Regulatory Oversight of Clinical AI Tools

10:30am - 11:30am Tuesday, March 10
Orlando - Orange County Convention Center
W230A

Description

The digitization of healthcare has accelerated the adoption of machine learning and artificial intelligence applications for clinical care. Access to real-world data from the electronic health record and clinical imaging systems has allowed for the development of novel predictive algorithms. The goal of these algorithms is to deliver on the promise of big data and precision medicine: increased efficiency, tailored therapies, and improved patient outcomes. With the growth in adoption, there has also come concern about the need for regulatory oversight in this rapidly evolving field. This session will explore proposed regulatory frameworks for clinical decision support and artificial intelligence applications in healthcare with use cases demonstrating best practices for the successful implementation of predictive models. Understanding these approaches will allow organizations to adopt these novel technologies while ensuring safe and effective clinical care.

Learning Objectives

  • Describe current and future regulatory requirements for validation and ongoing quality assessment of predictive models used for clinical decision support
  • Identify limitations of real-world data and approaches to reduce bias and error in machine learning models
  • Explain best practices for implementation and local validation of artificial intelligence and machine learning models used for clinical decision support

Speaker(s)

Assistant Professor, Dir of Informatics,
Lab Med, Yale School of Medicine
Professor of Medicine,
Yale University

Continuing Education Credits

ABPM
1.00
AHIMA
1.00
CAHIMS
1.00
CME
1.00
CNE
1.00
CPHIMS
1.00

Audience

CMIO/CMO
Data Scientist
Government or Public Policy Professional

Level

Intermediate